Automation of in-silico data analysis processes through workflow management systems

Data integration is needed in order to cope with the huge amounts of biological information now available and to perform data mining effectively. Current data integration systems have strict limitations, mainly due to the number of resources, their size and frequency of updates, their heterogeneity and distribution on the Internet. Integration must therefore be achieved by accessing network services through flexible and extensible data integration and analysis network tools. EXtensible Markup Language (XML), Web Services and Workflow Management Systems (WMS) can support the creation and deployment of such systems. Many XML languages and Web Services for bioinformatics have already been designed and implemented and some WMS have been proposed. In this article, we review a methodology for data integration in biomedical research that is based on these technologies. We also briefly describe some of the available WMS and discuss the current limitations of this methodology and the ways in which they can be overcome.     

微生物大片段DNA染色体整合技术研究进展

现代生物发酵工业聚焦于设计和创制高效的微生物细胞工厂,以实现原料向目标产品的定向转化。评判微生物细胞工厂性能优劣的主要标准是其合成能力及稳定性。由于质粒系统存在拷贝数不稳定、易于丢失等局限性,在菌株改造中将基因或产物合成途径整合至染色体上实现稳定表达通常是更优的选择。因此,染色体的基因整合技术作为实现这一目标的重要手段已受到广泛关注,并得到快速发展。本综述梳理了近年来微生物染色体上的大片段DNA整合方法的研究进展,归纳了各种技术的原理和特点,尤其是新兴的CRISPR相关转座系统,同时对未来的发展重点和方向进行了展望。     

Orymold: ontology based gene expression data integration and analysis tool applied to rice

Background  

Integration and exploration of data obtained from genome wide monitoring technologies has become a major challenge for many bioinformaticists and biologists due to its heterogeneity and high dimensionality. A widely accepted approach to solve these issues has been the creation and use of controlled vocabularies (ontologies). Ontologies allow for the formalization of domain knowledge, which in turn enables generalization in the creation of querying interfaces as well as in the integration of heterogeneous data, providing both human and machine readable interfaces.     

Bringing Web 2.0 to bioinformatics

Enabling deft data integration from numerous, voluminous andheterogeneous data sources is a major bioinformatic challenge.Several approaches have been proposed to address this challenge,including data warehousing and federated databasing. Yet despitethe rise of these approaches, integration of data from multiplesources remains problematic and toilsome. These two approachesfollow a user-to-computer communication model for data exchange,and do not facilitate a broader concept of data sharing or collaborationamong users. In this report, we discuss the potential of Web2.0 technologies to transcend this model and enhance bioinformaticsresearch. We propose a Web 2.0-based Scientific Social Community(SSC) model for the implementation of these technologies. Byestablishing a social, collective and collaborative platformfor data creation, sharing and integration, we promote a webservices-based pipeline featuring web services for computer-to-computerdata exchange as users add value. This pipeline aims to simplifydata integration and creation, to realize automatic analysis,and to facilitate reuse and sharing of data. SSC can fostercollaboration and harness collective intelligence to createand discover new knowledge. In addition to its research potential,we also describe its potential role as an e-learning platformin education. We discuss lessons from information technology,predict the next generation of Web (Web 3.0), and describe itspotential impact on the future of bioinformatics studies.      

Eugene--a domain specific language for specifying and constraining synthetic biological parts, devices, and systems

Background

Synthetic biological systems are currently created by an ad-hoc, iterative process of specification, design, and assembly. These systems would greatly benefit from a more formalized and rigorous specification of the desired system components as well as constraints on their composition. Therefore, the creation of robust and efficient design flows and tools is imperative. We present a human readable language (Eugene) that allows for the specification of synthetic biological designs based on biological parts, as well as provides a very expressive constraint system to drive the automatic creation of composite Parts (Devices) from a collection of individual Parts.

Results

We illustrate Eugene''s capabilities in three different areas: Device specification, design space exploration, and assembly and simulation integration. These results highlight Eugene''s ability to create combinatorial design spaces and prune these spaces for simulation or physical assembly. Eugene creates functional designs quickly and cost-effectively.

Conclusions

Eugene is intended for forward engineering of DNA-based devices, and through its data types and execution semantics, reflects the desired abstraction hierarchy in synthetic biology. Eugene provides a powerful constraint system which can be used to drive the creation of new devices at runtime. It accomplishes all of this while being part of a larger tool chain which includes support for design, simulation, and physical device assembly.     

Transparent mediation-based access to multiple yeast data sources using an ontology driven interface

Background

Over the past decade the workflow system paradigm has evolved as an efficient and user-friendly approach for developing complex bioinformatics applications. Two popular workflow systems that have gained acceptance by the bioinformatics community are Taverna and Galaxy. Each system has a large user-base and supports an ever-growing repository of application workflows. However, workflows developed for one system cannot be imported and executed easily on the other. The lack of interoperability is due to differences in the models of computation, workflow languages, and architectures of both systems. This lack of interoperability limits sharing of workflows between the user communities and leads to duplication of development efforts.

Results

In this paper, we present Tavaxy, a stand-alone system for creating and executing workflows based on using an extensible set of re-usable workflow patterns. Tavaxy offers a set of new features that simplify and enhance the development of sequence analysis applications: It allows the integration of existing Taverna and Galaxy workflows in a single environment, and supports the use of cloud computing capabilities. The integration of existing Taverna and Galaxy workflows is supported seamlessly at both run-time and design-time levels, based on the concepts of hierarchical workflows and workflow patterns. The use of cloud computing in Tavaxy is flexible, where the users can either instantiate the whole system on the cloud, or delegate the execution of certain sub-workflows to the cloud infrastructure.

Conclusions

Tavaxy reduces the workflow development cycle by introducing the use of workflow patterns to simplify workflow creation. It enables the re-use and integration of existing (sub-) workflows from Taverna and Galaxy, and allows the creation of hybrid workflows. Its additional features exploit recent advances in high performance cloud computing to cope with the increasing data size and complexity of analysis. The system can be accessed either through a cloud-enabled web-interface or downloaded and installed to run within the user's local environment. All resources related to Tavaxy are available at http://www.tavaxy.org.     

Building a BRIDGE for the integration of heterogeneous data from functional genomics into a platform for systems biology

The flood of data acquired from the increasing number of publicly available genomes has led to new demands for bioinformatics software. With the growing amount of information resulting from high throughput experiments new questions arise that often focus on the comparison of genes, genomes, and their expression profiles. Inferring new knowledge by combining different kinds of "post-genomics" data obviously necessitates the development of new approaches that allow the integration of variable data sources into a flexible framework. In this paper, we describe our concept for the integration of heterogeneous data into a platform for systems biology. We have implemented a Bioinformatics Resource for the Integration of heterogeneous Data from Genomic Explorations (BRIDGE) and illustrate the usability of our approach as a platform for systems biology for two sample applications.     

To engineer is to create: the link between engineering and regeneration

Tissue engineering is a radically different approach to reconstruction of the body following degenerative diseases, trauma or chronic debilitating conditions. Although there have been some successes, tissue engineering is not yet delivering significant progress in terms of clinical outcomes and commercialization. Part of the problem is that we have failed to understand what tissue engineering really means and to appreciate that engineering is synonymous with creation. These processes involve many different phases and there has been minimal integration of these phases within tissue-engineering paradigms. The conventional concept, based upon a temporal sequence from sourcing cells through to the incorporation of generated tissue into a host, has to be transformed by a systems engineering approach in which all biological and technological phases, and the inter-relationships between them, are fully integrated. It might be that real success will not be achieved until systems biology is superimposed onto this systems engineering paradigm.     

POEtic: an electronic tissue for bio-inspired cellular applications

In this paper, we introduce the general architecture of a new electronic tissue called POEtic. This reconfigurable circuit is designed to ease the implementation of bio-inspired systems that bring cellular applications into play. It contains special features that allow a developer to realize systems that require evolution (Phylogenesis), development (Ontogenesis), and/or learning (Epigenesis). A dynamic routing algorithm has been added to a structure similar to that of common commercial FPGAs, in order to allow the creation of data paths between cells. As the creation of these paths is dynamic, it is possible to add new cells or to repair faulty ones at runtime.     

Which coordinate system for modelling path integration?

Path integration is a navigation strategy widely observed in nature where an animal maintains a running estimate, called the home vector, of its location during an excursion. Evidence suggests it is both ancient and ubiquitous in nature, and has been studied for over a century. In that time, canonical and neural network models have flourished, based on a wide range of assumptions, justifications and supporting data. Despite the importance of the phenomenon, consensus and unifying principles appear lacking. A fundamental issue is the neural representation of space needed for biological path integration. This paper presents a scheme to classify path integration systems on the basis of the way the home vector records and updates the spatial relationship between the animal and its home location. Four extended classes of coordinate systems are used to unify and review both canonical and neural network models of path integration, from the arthropod and mammalian literature. This scheme demonstrates analytical equivalence between models which may otherwise appear unrelated, and distinguishes between models which may superficially appear similar. A thorough analysis is carried out of the equational forms of important facets of path integration including updating, steering, searching and systematic errors, using each of the four coordinate systems. The type of available directional cue, namely allothetic or idiothetic, is also considered. It is shown that on balance, the class of home vectors which includes the geocentric Cartesian coordinate system, appears to be the most robust for biological systems. A key conclusion is that deducing computational structure from behavioural data alone will be difficult or impossible, at least in the absence of an analysis of random errors. Consequently it is likely that further theoretical insights into path integration will require an in-depth study of the effect of noise on the four classes of home vectors.     

Probability landscapes for integrative genomics

Background  

The comprehension of the gene regulatory code in eukaryotes is one of the major challenges of systems biology, and is a requirement for the development of novel therapeutic strategies for multifactorial diseases. Its bi-fold degeneration precludes brute force and statistical approaches based on the genomic sequence alone. Rather, recursive integration of systematic, whole-genome experimental data with advanced statistical regulatory sequence predictions needs to be developed. Such experimental approaches as well as the prediction tools are only starting to become available and increasing numbers of genome sequences and empirical sequence annotations are under continual discovery-driven change. Furthermore, given the complexity of the question, a decade(s) long multi-laboratory effort needs to be envisioned. These constraints need to be considered in the creation of a framework that can pave a road to successful comprehension of the gene regulatory code.     

Evolutionary informatics: unifying knowledge about the diversity of life

The accelerating growth of data and knowledge in evolutionary biology is indisputable. Despite this rapid progress, information remains scattered, poorly documented and in formats that impede discovery and integration. A grand challenge is the creation of a linked system of all evolutionary data, information and knowledge organized around Darwin's ever-growing Tree of Life. Such a system, accommodating topological disagreement where necessary, would consolidate taxon names, phenotypic and geographical distributional data across clades, and serve as an integrated community resource. The field of evolutionary informatics, reviewed here for the first time, has matured into a robust discipline that is developing the conceptual, infrastructure and community frameworks for meeting this grand challenge.     

Nanobiotechnology and nanomedicine

Nanobiotechnology is a new direction in the technological science, which plays a key role in creation of nanodevices for analysis of living systems on a molecular level. Nanomedicine is the application of nanotechnologies in medicine for maintenance and improvement of human life using the knowledge on human organism at the molecular level. Application of nanoparticles and nanomaterials for the diagnostic and therapeutic purposes is now significantly extended in nanomedicine. Use of nanotechnological approaches and nanomaterials opens new prospects for creation of drugs and systems for their directed delivery. Implementation of optical biosensor, atomic force, nanowire and nanoporous approaches into genomics and proteomics will significantly enhance the sensitivity and accuracy of diagnostics and will shorten the time of diagnostic procedures that will undoubtedly improve the efficiency of medical treatment. The review highlights recent data on application of nanobiotechnologies in the field of diagnostics and creation of new drugs.     

Hybrid semi-parametric mathematical systems: bridging the gap between systems biology and process engineering

Systems biology is an integrative science that aims at the global characterization of biological systems. Huge amounts of data regarding gene expression, proteins activity and metabolite concentrations are collected by designing systematic genetic or environmental perturbations. Then the challenge is to integrate such data in a global model in order to provide a global picture of the cell. The analysis of these data is largely dominated by nonparametric modelling tools. In contrast, classical bioprocess engineering has been primarily founded on first principles models, but it has systematically overlooked the details of the embedded biological system. The full complexity of biological systems is currently assumed by systems biology and this knowledge can now be taken by engineers to decide how to optimally design and operate their processes. This paper discusses possible methodologies for the integration of systems biology and bioprocess engineering with emphasis on applications involving animal cell cultures. At the mathematical systems level, the discussion is focused on hybrid semi-parametric systems as a way to bridge systems biology and bioprocess engineering.     

医院全成本核算数据集成化管理的探讨

面对目前医院全成核算系统存在的各种弊端及困难,通过依托内部运行机制及现有的各项数据资源进行合理整合,无缝地将不同系统间的数据,按照统一的接口规则、标准的调控规范,采用科学的方法进行融合,实现全成本核算数据的高度集成化管理。利用科学的方法路线与技术路线详细分析并阐述了数据集成化的构建内容、集成化的管理作用,并对集成化管理的战略影响进行了必要的展望。     

Tools for managing and analyzing microarray data

The microarray-based analysis of gene expression has become a workhorse for biomedical research. Managing the amount and diversity of data that such experiments produce is a task that must be supported by appropriate software tools, which led to the creation of literally hundreds of systems. In consequence, choosing the right tool for a given project is difficult even for the expert. We report on the results of a survey encompassing 78 of such tools, of which 22 were inspected in detail and seven were tested hands-on. We report on our experiences with a focus on completeness of functionality, ease-of-use, and necessary effort for installation and maintenance. Thereby, our survey provides a valuable guideline for any project considering the use of a microarray data management system. It reveals which tasks are covered by mature tools and also shows that important requirements, especially in the area of integrated analysis of different experimental data, are not yet met satisfyingly by existing systems.     

Using the Unified Modelling Language (UML) to guide the systemic description of biological processes and systems

One of the main issues in Systems Biology is to deal with semantic data integration. Previously, we examined the requirements for a reference conceptual model to guide semantic integration based on the systemic principles. In the present paper, we examine the usefulness of the Unified Modelling Language (UML) to describe and specify biological systems and processes. This makes unambiguous representations of biological systems, which would be suitable for translation into mathematical and computational formalisms, enabling analysis, simulation and prediction of these systems behaviours.     

Owlifier: Creating OWL-DL ontologies from simple spreadsheet-based knowledge descriptions

Discovery and integration of data is important in many ecological studies, especially those that concern broad-scale ecological questions. Data discovery and integration are often difficult and time consuming tasks for researchers, which is due in part to the use of informal, ambiguous, and sometimes inconsistent terms for describing data content. Ontologies offer a solution to this problem by providing consistent definitions of ecological concepts that in turn can be used to annotate, relate, and search for data sets. However, unlike in molecular biology or biomedicine, few ontology development efforts exist within ecology. Ontology development often requires considerable expertise in ontology languages and development tools, which is often a barrier for ontology creation in ecology. In this paper we describe an approach for ontology creation that allows ecologists to use common spreadsheet tools to describe different aspects of an ontology. We present conventions for creating, relating, and constraining concepts through spreadsheets, and provide software tools for converting these ontologies into equivalent OWL-DL representations. We also consider inverse translations, i.e., to convert ontologies represented using OWL-DL into our spreadsheet format. Our approach allows large lists of terms to be easily related and organized into concept hierarchies, and generally provides a more intuitive and natural interface for ontology development by ecologists.     

Fiber-optic fluorescence imaging

Optical fibers guide light between separate locations and enable new types of fluorescence imaging. Fiber-optic fluorescence imaging systems include portable handheld microscopes, flexible endoscopes well suited for imaging within hollow tissue cavities and microendoscopes that allow minimally invasive high-resolution imaging deep within tissue. A challenge in the creation of such devices is the design and integration of miniaturized optical and mechanical components. Until recently, fiber-based fluorescence imaging was mainly limited to epifluorescence and scanning confocal modalities. Two new classes of photonic crystal fiber facilitate ultrashort pulse delivery for fiber-optic two-photon fluorescence imaging. An upcoming generation of fluorescence imaging devices will be based on microfabricated device components.